Normalised image ("stretched histogram")

While this is often a good technique for correcting the contrast and exposure in a photograph, it was clearly not doing the job for my strange attractor images. This was because the value histogram is extremely skewed to the left. Ie; most of the pixels are in the very (very) dark range, with only a few very bright pixels. In fact when viewed on-screen, most of these dark pixels are indistinguishable from black. Fortunately these are 16 bit images, so the hidden details are there to be discovered.

That’s where Histogram equalization comes in useful.

Histogram equalized

This technique actually redistributes the value densities along the histogram so that each value range is more evenly represented. Peaks in the histogram are widened and the troughs condensed. Effectively the surplus of dark shades in my images can be spread up towards the right of the value histogram.

As you can see in these examples of the same image first normalised, and then equalized, equalization really brings these images to life. Note that the grainy texture is due to the number of samples taken and not from the equalization process.

As I wanted to be able to quickly assess a folder full of thumbnails for interesting imaged, it made sense to find a way to programmatically equalize the images in Python before they are first saved to disk.

Luckily I found a suitable algorithm on Salem’s Vision Blog. I just had to make some small alterations so it would work with 16 bit images. I also set the first bin of the histogram to zero – this is to negate the large amount of black background which would otherwise ruin the equalization effect.ote

Here is my version of this code:

 def histeq(im,nbr_bins=2**16):
    '''histogram equalization'''
    #get image histogram
    imhist,bins = np.histogram(im.flatten(),nbr_bins,normed=True)
    imhist[0] = 0

    cdf = imhist.cumsum() #cumulative distribution function
    cdf ** .5
    cdf = (2**16-1) * cdf / cdf[-1] #normalize
    #cdf = cdf / (2**16.)  #normalize

    #use linear interpolation of cdf to find new pixel values
    im2 = np.interp(im.flatten(),bins[:-1],cdf)

    return np.array(im2, int).reshape(im.shape)
 

If you’d rather used an image manipulation program to do this, the effect is available in Photoshop. I believe it is found under Levels >> Adjustments >> Equalize. If you select an area of the image first the equalization for the entire image can be based on the histogram of just the selected portion. This can be very useful for negating a black background, or a white sky etc.

In Linux you can do the same thing with ImageJ, including selecting a region to base the histogram on.

GIMP and CinePaint also have an equalize feature, but no option that I could find to base the histogram on a selected portion of the image. Also GIMP is currently geared towards 8-bit images, so no good in this case.

And for the sake of completeness, here is the latest version of my strange attractor finder. I have tidied up the code and made several optimizations for speed:

#! /usr/bin/python

import png
import random
from math import floor
import numpy as np
import operator

def histeq(im,nbr_bins=2**16):
    '''histogram equalization'''
    #get image histogram
    imhist,bins = np.histogram(im.flatten(),nbr_bins,normed=True)
    imhist[0] = 0

    cdf = imhist.cumsum() #cumulative distribution function
    cdf ** .5
    cdf = (2**16-1) * cdf / cdf[-1] #normalize
    #cdf = cdf / (2**16.)  #normalize

    #use linear interpolation of cdf to find new pixel values
    im2 = np.interp(im.flatten(),bins[:-1],cdf)

    return np.array(im2, int).reshape(im.shape), cdf

def test_render(x_coefficients, y_coefficients,
                size, render_max, trap_size):

    # starting xy_pos
    x, y = random.random(), random.random()

    # initialize array
    img_array = np.zeros([size, size], int)

    a, b, c, d, e, f = x_coefficients
    g, h, i, j, k, l = y_coefficients

    while True:
        # main rendering loop

        x, y = (a*(x**2) + b*x*y + c*(y**2) + d*x + e*y + f,
                g*(x**2) + h*x*y + i*(y**2) + j*x + k*y + l)

        # translate x, y into array indices
        px = int((x+trap_size)*(size/(trap_size*2)))
        py = int((y+trap_size)*(size/(trap_size*2)))

        if not(px and py):
            return (None, None)
            # catch negative numbers... the "IndexError" won't
            # do this resulting in "wrap-around" images

        try:
            img_array[px, py] += 1
        except IndexError:
            return (None, None)

        if img_array[px, py] == render_max:
            return img_array, (x, y)

def final_render(x_coefficients, y_coefficients,
                size, render_max, trap_size, xy_pos):

    # starting xpos
    x, y = xy_pos

    # initialize array
    img_array = np.zeros([size, size], int)

    a, b, c, d, e, f = x_coefficients
    g, h, i, j, k, l = y_coefficients

    while True:
        # optimised rendering loop with no error checking etc.

        x, y = (a*(x**2) + b*x*y + c*(y**2) + d*x + e*y + f,
                g*(x**2) + h*x*y + i*(y**2) + j*x + k*y + l)

        # translate x, y into array indices
        px = int((x+trap_size)*(size/(trap_size*2)))
        py = int((y+trap_size)*(size/(trap_size*2)))

        # don't test for negatives

        # don't catch exceptions...
        img_array[px, py] += 1

        if img_array[px, py] == render_max:
            return img_array

if __name__ == "__main__":

    import psyco
    psyco.full()

    trap_range = 2  # potential attractor is rejected if xy_pos
                    # falls further than this distance from origin

    painted_area_threshold = .01 # reject attractor if less than this portion
                                 # the whole has been painted
    test_render_size = 200
    test_render_max = 200

    final_render_size = 800
    final_render_max = 100000

    equalize_final = True

    img_index = 0
    imgWriter = png.Writer(final_render_size, final_render_size,
                           greyscale=True, alpha=False, bitdepth=16)

    while True:

        # Set the tweakable values of the attractor equations:
        random.seed()
        seed = int(random.random() * 10000000)
        random.seed(seed)
        x_coefficients = [random.choice([random.random()*4-2, 0, 1])\
                           for x in range(6)]
        y_coefficients = [random.choice([random.random()*4-2, 0, 1])\
                           for x in range(6)]

        # perform small test render
        img_array, xy_pos = test_render(x_coefficients, y_coefficients,
                                        test_render_size, test_render_max,
                                        trap_range)

        #only proceed if test attractor did not fail
        if not xy_pos:
            continue

        #only proceed if painted area meets threshold, otherwise loop to top
        painted_area = sum(img_array[img_array==True].flatten()) / test_render_size ** 2.
        if painted_area < painted_area_threshold:
            continue

        # perform final render
        print "rendering: %06d_%d.png" % (img_index, seed)
        try:
            img_array = final_render(x_coefficients, y_coefficients,
                                     final_render_size, final_render_max,
                                     trap_range, xy_pos)
        except IndexError:
            continue

        # histogram equalization
        if equalize_final:
            img_array, cdr = histeq(img_array)

        # save to final render to png file
        f = open("a_%06d_%d.png" % (img_index, seed), "wb")
        imgWriter.write(f, img_array)
        f.close()
        img_index += 1
        print "SAVED!"
 

GIMP is implementing 16 -bit support piecewise,  with full support slated for version 3. This scuttles my plan to work with high range grayscale images in GIMP.

Krita could have been a solution but I can’t try that as on my system (Ubuntu 9.10) it’s broken.

CinePaint adjusts the levels beautifully but I can’t find how to map the greyscale to an arbitrary colored gradient.

ImageJ specialises in scientific analysis of image data. It was able to fix the contrast and apply “lookup table” coloration (see below). The finished image has a palette of 256 colours only because of the way the lookup table feature works, but It still looks miles better than my previous attempts using GIMP exclusively.

http://bigwww.epfl.ch/teaching/projects/abstracts/geroudet/

Abstract

In the biomedical field, specialists need to work on series of images taken under a microscope, covering an area interesting to analyze. The purpose of this project was, starting from a lot of images forming a mosaic, to build an intuitive navigation system, which could maintain the quality of basic images. We developed two plug-in for images: the first helps to prepare images, while the second deals with the navigation in the mosaic of images. The overall strategy used here was to cut basic images into small blocks at different zoom levels. Then, when displaying, the second plug- in is able to retrieve the blocks corresponding to the area considered by the user. Note that zooms are made using B-Splines.

Fig. 1: User interface for the plug- in preparation	Fig. 2: User interface for the navigation in the mosaic
Fig. 3: Example of use plugin navigation

http://bigwww.epfl.ch/algorithms.html

http://www.google.com/codesearch/p?hl=fr&sa=N&cd=4&ct=rc#M0QGbzpICpo/kybic/thesis/&q=MRI%20mesh%20matlab The algorithms below are ready to be downloaded. They are generally written in JAVA or in ANSI-C, either by students or by the members of the Biomedical Imaging Group.Please contact the author of the algorithms if you have a specific question.

JAVA: Plug-ins for ImageJ
JAVA classes are usually meant to be integrated into the public-domain software ImageJ.
	Drop Shape Analysis. New method based on B-spline snakes (active contours) for measuring high-accuracy contact angles of sessile drops.
	Extended Depth of Focus. The extended depth of focus is a image-processing method to obtain in focus microscopic images of 3D objects and organisms. We freely provide a software as a plugin of ImageJ to produce this in-focus image and the corresponding height map of z-stack images.
	Fractional spline wavelet transform. This JAVA package computes the fractional spline wavelet transform of a signal or an image and its inverse.
	Image Differentials. This JAVA class for ImageJ implements 6 operations based on the spatial differentiation of an image. It computes the pixel-wise gradient, Laplacian, and Hessian. The class exports public methods for horizontal and vertical gradient and Hessian operations (for those programmers who wish to use them in their own code).
	MosaicJ. This JAVA class for ImageJ performs the assembly of a mosaic of overlapping individual images, or tiles. It provides a semi-automated solution where the initial rough positioning of the tiles must be performed by the user, and where the final delicate adjustments are performed by the plugin.
	NeuronJ. This Java class for ImageJ was developed to facilitate the tracing and quantification of neurites in two-dimensional (2D) fluorescence microscopy images. The tracing is done interactively based on the specification of end points; the optimal path is determined on the fly from the optimization of a cost function using Dijkstra’s shortest-path algorithm. The procedure also takes advantage of an improved ridge detector implemented by means of a steerable filterbank.
	PixFRET. The ImageJ plug-in PixFRET allows to visualize the FRET between two partners in a cell or in a cell population by computing pixel by pixel the images of a sample acquired in three channels.
	Point Picker. This JAVA class for ImageJ allows the user to pick some points in an image and to save the list of pixel coordinates as a text file. It is also possible to read back the text file so as to restore the display of the coordinates.
	Resize. This ImageJ plugin changes the size of an image to any dimension using either interpolation, or least-squares approximation.
	SheppLogan. The purpose of this ImageJ plugin is to generate sampled versions of the Shepp-Logan phantom. Their size can be tuned.
	Snakuscule. The purpose of this ImageJ plugin is to detect circular bright blobs in images and to quantify them. It allows one to keep record of their location and size.
	SpotTracker Single particle tracking over noisy images sequence. SpotTracker is a robust and fast computational procedure for tracking fluorescent markers in time-lapse microscopy. The algorithm is optimized for finding the time-trajectory of single particles in very noisy image sequences. The optimal trajectory of the particle is extracted by applying a dynamic programming optimization procedure.
	StackReg. This JAVA class for ImageJ performs the recursive registration (alignment) of a stack of images, so that each slice acts as template for the next one. This plugin requires that TurboReg is installed.
	Steerable feature detectors. This ImageJ plugin implements a series of optimized contour and ridge detectors. The filters are steerable and are based on the optimization of a Canny-like criterion. They have a better orientation selectivity than the classical gradient or Hessian-based detectors.
	TurboReg. This JAVA class for ImageJ performs the registration (alignment) of two images. The registration criterion is least-squares. The geometric deformation model can be translational, conformal, affine, and bilinear.
	UnwarpJ. This JAVA class for ImageJ performs the elastic registration (alignment) of two images. The registration criterion includes a vector-spline regularization term to constrain the deformation to be physically realistic. The deformation model is made of cubic splines, which ensures smoothness and versatility.
ANSI C
Most often, the ANSI-C pieces of code are not a complete program, but rather an element in a library of routines.
	Affine transformation. This ANSI-C routine performs an affine transformation on an image or a volume. It proceeds by resampling a continuous spline model.
	Registration. This ANSI-C routine performs the registration (alignment) of two images or two volumes. The criterion is least-squares. The geometric deformation model can be translational, rotational, and affine.
	Shifted linear interpolation. This ANSI-C program illustrates how to perform shifted linear interpolation.
	Spline interpolation. This ANSI-C program illustrates how to perform spline interpolation, including the computation of the so-called spline coefficients.
	Spline pyramids. This software package implements the basic REDUCE and EXPAND operators for the reduction and enlargement of signals and images by factors of two based on polynomial spline representation of the signal.
Others
	E-splines. A Mathematica package is made available for the symbolic computation of exponential spline related quantities: B-splines, Gram sequence, Green function, and localization filter.
	Fractional spline wavelet transform. A MATLAB package is available for computing the fractional spline wavelet transform of a signal or an image and its inverse.
	Fractional spline and fractals. A MATLAB package is available for computing the fractional smoothing spline estimator of a signal and for generating fBms (fractional Brownian motion). This spline estimator provides the minimum mean squares error reconstruction of a fBm (or 1/f-type signal) corrupted by additive noise.
	Hex-splines : a novel spline family for hexagonal lattices. A Maple 7.0 worksheet is available for obtaining the analytical formula of any hex-spline (any order, regular, non-regular, derivatives, and so on).
	MLTL deconvolution : This Matlab package implements the MultiLevel Thresholded Landweber (MLTL) algorithm, an accelerated version of the TL algorithm that was specifically developped for deconvolution problems with a wavelet-domain regularization.
	OWT SURE-LET Denoising : This Matlab package implements the interscale orthonormal wavelet thresholding algorithm based on the SURE-LET (Stein’s Unbiased Risk Estimate/Linear Expansion of Thresholds) principle.
	WSPM : Wavelet-based statistical parametric mapping, a toolbox for SPM that incorporates powerful wavelet processing and spatial domain statistical testing for the analysis of fMRI data.

]]> http://cieloazulgris.wordpress.com/2009/09/12/algo-util-para-algunos/ Sat, 12 Sep 2009 17:44:00 +0000 dannygalleguillos http://cieloazulgris.wordpress.com/2009/09/12/algo-util-para-algunos/

• 4Peaks: Visualización de cromatogramas de distintos equipos de secuenciación de DNA. Entrega histogramas de calidad y conexión directa a Blast
• EnzymeX: Permite realizar análisis de restricción de fragmentos de DNA. Además posee una completa base de datos de las enzimas de restricción disponibles en el mercado, permitiendo planificar el experimento y visualizar el resultado esperado

• EdenGraph: Un calculador de gráficos en 2D dinámico. Simple de usar. 
• ImageJ: El clásico procesador de imágenes. Pero en este enlace, con un paquete de plug-ins seleccionados especialmente para el análisis de imégenes de microscopía
• Plot0.997: Nuevamente gráficos en 2D. Poderoso y con amplias funciones.
• Chmox: Un visualizador de archivos .chm. Muchas guías y manuales de ayuda están en este formato.

Adios!

The requirements for the flight strictly called for oblique angles.  I settled on 45 degrees for most of them, though occasionally I used a steeper or a shallower angle as best suited the subject.  But I took some ortho images anyway.  It’s always fun to compare against what Google Earth shows for a particular region, and ortho can be useful when I’m going through the pictures later to judge distance and height.  The field of view of my camera is such that the horizontal width of the frame corresponds to the height of the camera to within 5%.  By taking the occasional orthogonal image, I can typically go back through the images with a map or Google Earth to figure out how high the camera was.  Besides, it’s always fun to compare the KAP image to the Google Earth image, if for no other reason than to point out that comparing satellite imagery and low altitude aerial imagery is seriously comparing apples and oranges.

Ever since doing some work for an archaeologist from Oahu, I’ve been trying various false color techniques to try to get information out of my images.  Most of the time this has been done with orthogonal pictures, though as you can see from my previous post, the same tricks can be used on practically any picture.  But it works well with the orthos:

There are a number of features in this picture that are worth trying to isolate.  There are kiawe trees, a patch of fountain grass, pahoehoe lava from a Mauna Loa lava flow, aa lava from a Hualalai lava flow, water, coral rock, and even a swimmer in the water.

The first approach I tried was to use ImageJ with the DStretch plug-in.  It’s a really good false color filter that can be used to boost any manner of color combinations in an image.  In this case I used the YDS setting with a 315 degree rotation in hue, and managed to isolate most of the features listed above:  Kiawe trees are rendered as a combination of yellowish green in the upper branches, and red for the dead undergrowth.  The fountain grass is rendered as a bright red.  Pahoehoe lava is rendered as a blackish purple, and the aa is rendered as a reddish purple, as are the cracks in the pahoehoe lava.  Coral shows up as  a very light blueish purple, almost white, and the water is rendered as antifreeze green.  Lurid though the colors may be, it makes picking out individal elements in the image quite easy.

Another approach that has worked well in the past is to separate the R, G, and B channels in the image, and treat them as components in a mathematical expression.  This is a tried and true technique that’s been used in astronomy for ages.  B-V (or in the RGB world, B-G) images can be used to judge the temperature of a star, for example.  I wasn’t taking pictures of stars, but there’s still validity in the idea.  Let’s say I want to find the redder aa lava, but don’t want to get a false positive from the coral rock in the frame.  Subtracting the blue channel from the red channel picks up primarily red objects, in this case aa lava and cracks in the darker pahoehoe lava, though this catches the green vegetation as well:

Similarly, green vegetation can be isolated by subtracting red or blue from the green component, and in this case it doesn’t do such a good job of picking up the rocks:

Slightly more complicated expressions can be used to isolate other colors in the image, or to further separate colors.  And likewise, this trick can be used on false color images that have been produced through some other tool, such as DStretch.

Despite having resources like Google Earth available to us, kite aerial photography still offers a great deal of utility as a remote sensing platform.  But I still like making pretty pictures best of all.  The next time I grab my bag and head out the door, it’ll be to go somewhere nice and take pictures I like: the kind I want to hang on my wall.

– Tom

With an airplane, stereo photography is typically done by pointing the camera 90 degrees to the direction of flight, and taking a succession of pictures as the airplane flies across the landscape.  Careful choice of frame rate, airspeed, and altitude yields good results.

With a kite, a similar technique can be used:  Point the camera 90 degrees to the kite line, start the shutter going, and carefully walk backwards.  The kite will quickly settle into a stable flight angle with the increased apparent wind speed from the walking, and a nice steady stream of pictures is the result.  This is an example from a recent flight over the contact between two lava flows on the Big Island of Hawaii:

The top two pictures are the natural color images as they came off the camera.  The bottom two require more explanation:

Another technique I’ve used with aerial photography is to apply false color techniques to boost certain details, certain colors, or to change the contrast of the image so that particular features will catch the eye.  Most of my experimentation along these lines has been done using an image manipulation program called ImageJ, and a plug-in called DStretch.  ImageJ is a general purpose image manipulation program written in Java.  DStretch is an implementation of the principal component algorithm for contrast stretching that was written by Jon Harman.  It was originally written for bringing up faint details in pictographs, but it has proven to be useful with aerial imagery as well.

In order to get a sense of scale of the image, the thin yellow line in the top pair is a three-section 25′ painter’s pole thath as been collapsed to its shortest length.  My flying partner and I were using it to lower a camera into one of the holes we found in the lava in order to explore the inside.

This probably isn’t the best example of either technique, but it’s the first time I used them in combination.  At some point I’ll write a more in-depth article about building stereo pairs, and a second article about the use of ImageJ and DStretch with aerial photography.  In the meanwhile, enjoy.

Tom

http://www.plosone.org/static/figureGuidelines.action

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Figures can have a width between 8.25 cm and 17.15 cm and a maximum height of 23.5 cm. If your figures have labels that are in 8 point type or if your figures are very detailed, it is recommended that your figure be created so that it will span two columns.

Article Type

• 2-column: Research Article, Expert Commentary, Guidelines and Guidance, Learning Forum, Neglected Diseases, PLoS Medicine Debate, Primer, Review, Symposium.
• 3-column: Editorial, Education, Essay, Health In Action, Historical and Philosophical Perspectives, Historical Profiles and Perspectives, Interview, Message from ISCB, Opinion, Perspective, Policy Forum, Policy Platform, Research In Translation, Special Report, Viewpoint.

7. Figure Types

Line Art

Line art has sharp, clean lines and geometrical shapes against a white background. Line art is typically used for tables, charts, graphs, and gene sequences. You can use a program like Illustrator to create high-quality line art. A minimum resolution of 300 dpi will maintain the crisp edges of the lines and shapes.

• Format: EPS or TIFF
• Minimum Resolution: 300 dpi

Grayscale

Grayscale figures contain varying tones of black and white. They contain no color, so grayscale is synonymous with “black and white.” The gray scale is divided into 256 sections with black at 0 and white at 255. Software for preparation of grayscale art includes Photoshop.

• Format: EPS or TIFF
• Minimum Resolution: 300 dpi

Halftones

The best example of a halftone is a photograph, but halftones include any image that uses continuous shading or blending of colors or grays, such as gels, stains, microarrays, brain scans, and molecular structures. To prepare and manipulate halftone images, use Photoshop or a comparable photo-editing program.

• Format: TIFF
• Minimum Resolution: 300 dpi

Combination Figures

Combination figures contain two or more types of images, for example, a halftone figure containing text. You should embed the images, group the objects, or flatten the layers, and flatten transparencies before saving as TIFF at a minimum of 300 dpi.

• Format: TIFF
• Minimum Resolution: 300 dpi

Stereograms

Stereograms are figures with two almost identical pictures placed side by side which, when viewed through special glasses or a stereoscope, produce a three-dimensional image.

If you plan on submitting a stereogram as one of your figures, make sure this is clearly mentioned in the caption for the figure within the manuscript. Stereograms must be sized so that the centers of each of these images are 63 mm apart. Make sure that the stereogram figure is at the size you would like them to display. They will be checked prior to publishing, but this step will ensure your stereogram will be viewed properly.

8. Uploading Figures to the PLoS Manuscript Submission System

Upload Order

• Upload cover letter, then article file first. Ensure that it contains the figure legends, but not the figures themselves.
• Figures should be numbered in the order they are first mentioned in the text, and uploaded in the same order. For example, Figure 1 should be uploaded as the first figure file, Figure 2 the second, etc.
• Figures should be uploaded in the desired orientation.
• Multimedia files (.avi or .swf files) must be uploaded as a Supporting Information file type and not a figure. See Multimedia Files below for more information.

Note: When a figure is uploaded to the PLoS manuscript submission system, a PDF file is created that contains the image but does not represent the final appearance of your figures in your published article. In addition, a “merged PDF” containing the article file and all of the figures is created automatically, which should be used by authors as a quick way to review their figures for egregious errors.

9. Multimedia Files

PLoS encourages authors to submit multimedia files that are crucial to the conclusions of the paper. Multimedia files should be smaller than 10 MB because of the difficulties that some users will experience in loading or downloading files. These files are published as Supporting Information. Preferred formats are:

• Audio: MP3
• Video: MOV, progressive download, 320 x 240 px frame size
• Flash: SWF

10. Image Manipulation

Image files should not be manipulated or adjusted in any way that could lead to misinterpretation of the information present in the original image. Inappropriate manipulation includes but is not limited to:

• The introduction, enhancement, movement, or removal of specific feature(s) within an image;
• Unmarked grouping of images that should otherwise have been presented separately (for example, from different parts of the same gel, or from different gels, fields, or exposures);
• Adjustments of brightness, contrast, or color balance that obscure, eliminate, or misrepresent any information.

Digital images in manuscripts nearing acceptance for publication may be scrutinized for any indication of improper manipulation. If evidence is found of inappropriate manipulation we reserve the right to ask for original data and, if that is not satisfactory, we may decide not to accept the manuscript.

We are grateful to staff at the Journal of Cell Biology (Rockefeller University Press) for their help in establishing these guidelines and procedures (http://www.jcb.org/misc/ifora.shtml#image_aquisition)

11. How To

Embed Fonts in EPS Files

Always embed fonts or create outlines when creating EPS files. If your figures require special symbols and Greek characters the text may not reproduce properly unless you embed your fonts or create outlines of the text. See the Convert Text to Outlines below for more information.

To embed fonts using Adobe Illustrator, open the EPS file. From the File Menu, select Save As. In the Save As dialog box, make sure that the Embed Fonts option is selected and click OK.

Convert Text to Outlines

When you convert text to outlines, the text is converted to a series of lines and fills. The reference to the font that was used to create the text is no longer present. This process makes it unnecessary for the PLoS production department to have the original font used to create the figure text. This is to ensure that your figures publish as you intended them to.

Example of text that has not been converted to outlines.	Example of text that has been converted to outlines. Notice that every character is outlined.

You can use Adobe Illustrator to convert text to outlines by selecting the text you want to convert. Then from the Type menu, select Create Outlines (Shift + Control + O on PC, and Shift + Apple + O on Mac).

If you do not convert text to outlines, when your figure is opened during the production process any text in a non-standard font will automatically be substituted for default font. This can cause the text in the figure to render incorrectly.

Caution: You will not be able to change your text after it has been converted to outlines so make sure it is correct before converting.

Convert Other File Types to TIFF

Convert PDF to TIFF Using Photoshop

1. Open the PDF file in Photoshop and select the page of the PDF that contains the figures to save as TIFF.
2. From the File menu, select Save As to open the Save As dialog box.
3. In the Save As dialog box, select TIFF from the Format dropdown list.
4. When the TIFF Options dialog box displays, make sure to check the LZW compression checkbox.
5. Click OK.

Convert EPS, JPG, GIF, or Other File Types to TIFF Using Photoshop

1. Open the figure file in Photoshop.
2. From the File menu, select Save As to open the Save As dialog box.
3. In the Save As dialog box, select TIFF from the Format drop down list.
4. When the TIFF Options dialog box displays, make sure to check the LZW compression checkbox.
5. Click OK.

Note: Do not use the “optimize for web” wizard for any figures. Some programs may down sample your images to low resolution.

Convert PDF to TIFF Using Adobe Illustrator

1. Open the PDF file in Adobe Illustrator, select the PDF page to export and click OK.
2. From the File menu, select Export to display the Export dialog box.
3. From the Export dialog box, select TIFF from the Save as Type drop down list and click OK.
4. When the TIFF Options dialog displays, select LZW compression.
5. Click OK to complete the process.

Convert EPS to TIFF Using Illustrator

1. Open the EPS file in Adobe Illustrator.
2. From the File menu, select Export to display the Export dialog box.
3. From the Export dialog box, select TIFF from the Save as Type drop down list and click OK.
4. When the TIFF Options dialog displays, select LZW compression.
5. Click OK to complete the process.

Convert PowerPoint Files to High-Resolution TIFFs Using Adobe Acrobat and Photoshop

Caution: Do not use File > Save as > TIFF. This will result in a low-resolution, poor-quality figure.

Step I: Convert PowerPoint File to PDF

There are two possible ways to create PDFs from PowerPoint files: use the Adobe PDF menu in some versions of PowerPoint, or create a PDF via the Print command.

1. Open your file in PowerPoint. From the Adobe PDF menu, select Change Conversion Settings. The PDFMaker Settings dialog displays.
2. From the Conversion settings dropdown menu, select High Quality and click OK.
3. From the Adobe PDF menu, select Convert to Adobe PDF. You will be asked to save the PDF file to a location of your choosing.
4. Click OK.

– OR -

1. Open your file in PowerPoint.
2. Select Print from the File dropdown menu.
3. Select the PDFCreator or similar tool in the Printer Name window.
4. Click OK.

Note: If your PowerPoint file contains figures on multiple slides, after you create the PDF file you will need to use Adobe Acrobat to separate the figures/slides into individual files. You can also use PowerPoint to create separate files of each figure/slide.

Step II: Convert Multi-Page PDF File to Individual Files

1. Using Adobe Acrobat Standard, open the PDF file that you created in Step 1. From the Document menu, select Pages and then Extract. The Extract Page dialog box displays.
2. Enter the page numbers in the To and From fields and then select the Delete Pages checkbox. Checking this box will delete the page that you entered in the To and From fields from the PDF file.
3. Click OK. The page that you specify in the previous step is now shown in Acrobat.
4. From the File menu, select save and enter the file name (e.g., Figure 1) for the extracted page and then click OK.
5. Repeat this process until a separate file is created for each figure/slide.

Step III: Convert Individual PDF Files to TIFFs

1. Using Photoshop, open the PDF file that you created in Step II.
2. From the File menu, select Save As.
3. From the Save As dialog box, select TIFF from the Format dropdown list and click Save.
4. In the TIFF Options dialog box, make sure the following options are selected. Under Image Compression, select LZW and under Pixel Order, select Interleaved.
5. Click OK.
6. Repeat this process until a separate TIFF file is created for each figure/slide.

Reduce TIFF File Size with LZW Compression

PLoS has a strict 10 MB figure file limit. To reduce the size of your figure, open your TIFF files in Photoshop. From the File menu, select Save As to open the Save As dialog box. In the Save As dialog box, select TIFF from the Format dropdown list. When the TIFF Options dialog box displays, make sure to check the LZW compression checkbox. Click OK.

Locate the Resolution Information in a TIFF File

You can locate the resolution of a figure file using Adobe Photoshop or through Windows Explorer.

Photoshop

To find the resolution of a figure using Photoshop, first open the file. Then from the Image menu, select Image Size. The Image Size dialog box will open displaying the figure dimensions, document size and resolution. You can decrease the size of a file, but you should not increase the resolution and/or dimensions of a file to meet the journals requirements. Increasing the file sizes manually may result in poor quality figures.

Windows Explorer

To check the resolution of a figure file using Windows Explorer, locate and select the file. Right-click and select Properties. In the Properties dialog box, select the Summary Tab. If you do not see the properties of the figures, click Advanced. This will display all of the properties associated with the selected figure. Look at the Horizontal Resolution and Vertical Resolution to determine the figure resolution.

12. Format Tables

Tables submitted for production should be included at the end of the article DOC or RTF file. For LaTeX submissions, table files should be uploaded individually into the online submission system. Tables that will be Supporting Information files can be submitted in any allowed format: Word, Excel, PDF, PPT, JPG, EPS, or TIFF.

Title and footnotes

Each table needs a concise title of no more than one sentence. The legend and footnotes should be placed below the table. Footnotes can be used to explain abbreviations.

Specifications

Tables that do not conform to the following requirements may give unintended results when published. Problems may include movements of data (rows or columns), loss of spacing, or disorganization of headings. Note: Multi-part tables with varying numbers of columns or multiple footnote sections should be divided and renumbered as separate tables.

Table requirements:

• Cell-based (e.g., created in Word with Tables tool or in Excel).
• Editable (i.e., not graphic object).
• Heading/subheading levels in separate columns.
• Size no larger than one printed page (7 in x 9.5 in). Larger tables can be published as online supporting information.
• No returns, tabs, or merged cells or rows.
• No color, shading, lines, or rules.
• No inserted text boxes or pictures.
• No tables within tables.

Examples

<!–

Example of incorrect subheads and use of text boxes Example of acceptable subheads within a table

–>

13. Getting Help

Contact If you have questions about your figures after reading the guidelines, you can email figures@plos.org.

Web 3.0 Multi-Media implosion and convergence

For starters have a look at:

• http://www.flickr.com/photos/thomcochrane/3215225090/
• http://thomcochrane.vox.com/library/post/ted-talks-now-on-iphone.html
• http://thomcochrane.vox.com/library/post/new-media-consortium-emerging-technologies-2009-report.html
• http://thomcochrane.vox.com/library/post/nokia-share-online-services.html

Then consider pixelpipe and how it allows you to converge:

• flickr + picassa + ovi + imagej + vox + juploadr + nokia-photos + many more

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Le J indique que le programme a été écrit en un langage “Java” qui en fait un logiciel utilisable sur différents systèmes d’exploitation (mac, pc Windows…). C’est un logiciel multiplateformes, en raison de son fonctionnement sur une machine virtuelle Java.

ImageJ est un logiciel libre : le code source est en accès libres et peut être modifié.  Ses fonctions sont extensibles ; de nombreux plug-ins existent, qui abordent des domaines jusque là réservés aux logiciels commerciaux comme Aphelion : manipulation et visualisation d’images tridimensionelles, filtrages médians et morphologiques 3D, contours actifs (snakes), filtres diffusifs… Par ailleurs, il est possible de combiner les fonctions natives ou ajoutées en créant des macros – la maîtrise de Java n’est pas alors nécessaire.

La foisonnante diversité des plug-ins disponibles – plus d’une centaine – en fait son avantage principal, mais peut dérouter les néophytes : il est parfois difficile de trouver rapidement une fonction correspondant à un besoin précis ou on en trouve une dizaine pour un problème donné!

ImageJ peut être téléchargé gratuitement sur le site du NCBI.

http://rsb.info.nih.gov/ij/

les plugins: http://rsb.info.nih.gov/ij/plugins/index.html

Il se présente sous la forme d’une barre de menus flottante qui ouvre des fenêtres de données, elles aussi flottantes.

Barre des menus flottante de ImageJ

La plupart des opérations courantes de traitement d’images sont réalisables avec ImageJ : visualisation et ajustement de l’histogramme des niveaux de gris, débruitage, correction d’éclairage, détection de contours, seuillage, opérations entre images…

Des traitements issus de la morphologie mathématique sont aussi disponibles : érosion/dilatation, ligne de partage des eaux, squelettisation… En analyse d’images, ImageJ permet de dénombrer des particules, d’évaluer leurs ratios d’aspect, de mesurer diverses grandeurs (distances, surfaces), d’extraire des coordonnées de contours… L’ajout personnalisé de fonctions est possible grâce aux plugins  à écrire en java.

MRI Cell Image Analyzer

Colocalization is usually evaluated by visual inspection of signal overlap or by using commercially available software tools, but there are limited possibilities to automate the analysis of large amounts of data.

–formation

www.picin.u-bordeaux2.fr/Cours/formation_2006/cerner_la_colocalisation_2006.pdf

—————————————————————–

–Colocalization image processing imageJ

Colocalisation analysis is an subject plagued with errors and contention. The literature is full of different methods for colocalisation analysis which probably reflects the fact that one approach does not necessarily fit all circumstances.

Analysis can be considered qualitative or quantitative. However, opinions differ as to which category the different approaches fall!

Qualitative analysis can be thought of as “highlighting overlapping pixels”. Although this is often given as a number (“percentage overlap”) suggesting quantification, the qualitative aspect arises when the user has to define what is considered “overlapping”. The two channels have a threshold set and any areas where they overlap is considered “colocalised”. Qualitative analysis has the benefit of being readily understood with little expert knowledge but suffers from the intrinsic user bias of “setting the threshold”. There are algorithms available which will automate the thresholding without user intervention but these rely on analysis of the image’s histogram which is subject to user intervention during acquisition.

Quantitative analysis removes user bias by analysing all the pixels based on of their intensity (it must be noted that some authors consider this a draw back rather than an advantage due to the intrinsic uncertainty of pixel intensity; see Lachmanovich et al. (2003) J. Microscopy, 212, 122-131). There are a number of coefficients detailed in the literature which can be calculated using ImageJ; each coefficient has it’s strengths and weaknesses and should be thoroughly researched before being used. It is this requirement for the coefficient to be fully understood which is a disadvantage when trying to convey information to research peers who are experts in biology, and not necessarily mathematics.

One key issue that can confound colocalisation analysis is bleed through. Colocalisation typically involves determining how much the green and red colours overlap. Therefore it is essential that the green emitting dye does not contribute to the red signal (typically, red dyes do not emit green fluorescence but this needs to be experimentally verified). One possible way to avoid bleed-through is to acquire the red and green images sequentially, rather than simultaneously (as with normal dual channel confocal imaging) and the use of narrow band emission filters. Single and unlabelled controls must be used to assess bleed-through.

Intensity Correlation Analysis

This plugin generates Mander’s coefficients (see below) as well as performing Intensity Correlation Analysis as described by Li et al. To fully understand this analysis you should read:
Li, Qi, Lau, Anthony, Morris, Terence J., Guo, Lin, Fordyce, Christopher B., and Stanley, Elise F. (2004). A Syntaxin 1, G{alpha}o, and N-Type Calcium Channel Complex at a Presynaptic Nerve Terminal: Analysis by Quantitative Immunocolocalization. Journal of Neuroscience 24, 4070-4081.

It is bundled with WCIF ImageJ and can be downloaded alone here.

reference:

http://www.uhnresearch.ca/facilities/wcif/imagej/colour_analysis.htm

“Manders’ Coefficient” (formerly Image Correlator plus<!–[if supportFields]> XE “Image Correlator plus: Wayne Rasband, Tony Collins“ <![endif]–> ” and “Red-Green Correlator” plugins)

This plugin generates various colocalisation coefficients for two 8 or 16-bit images or stacks.

The plugins generate a scatter plots plus correlation coefficients. In each scatter plot, the first (channel 1) image component is represented along the x-axis, the second image (channel 2) along the y-axis. The intensity of a given pixel in the first image is used as the x-coordinate of the scatter-plot point and the intensity of the corresponding pixel in the second image as the y-coordinate.

The intensities of each pixel in the “Correlation Plot” image represent the frequency of pixels that display those particular red/green values. Since most of you image will probably be background, the highest frequency of pixels will have low intensities so the brightest pixels in the scatter plot are in the bottom left hand corner – i.e. x~ zero, y ~ zero. The intensities in the “Red-Green correlation plot” image represent the actual colour of the pixels in the image.

Mito-DsRed; ER-EGFP

TMRE (red) plus Mito-pericam (Green)

Both plugins generate various colocalisation coefficients: Pearson’s (Rr), Overlap (R) and Colocalisation (M1, M2) See Manders, E.E.M., Verbeek, F.J. & Aten, J.A. ‘Measurement of co-localisation of objects in dual-colour confocal images’,  (1993) J. Microscopy, 169, 375-382. See tutorial sheet ‘Colocalisation’ for details. The threshold is also reported (0,0 means no threshold was used).

Colocalisation Test

When a coefficient is calculated for two images, it is often unclear quite what this means, in particular for intermediate values. This raises the following question: how does this value compare with what would be expected by chance alone?

There are several approaches that can be used to compare an observed coefficient with the coefficients of randomly generated images. Van Steensel (3) adopted an approach where the observed colocalisation between channel 1 and channel 2 was compared to colocalisation between channel 1 and a number of channel 2 images that had been translated (i.e. displaced by a number of pixels) in increments along the image’s X-axis. Fay et al (4) extended this approach by translating channel 2 in 5-pixel increments along the X- and Y-axis (i.e., –10, –5, 0, 5, and 10) and ± 1 slices in the Z-axis. This results in 74 randomisations (plus one original channel 2). The observed correlation was compared to these 74 and considered significant if it was greater than 95% of them.

Costes et al. (5) subsequently adopted a different approach, based on “scrambling” channel 2. The original channel 1 image was compared to 200 “scrambled” channel 2 images; the observed correlations between channel 1 and channel 2 were considered significant if they were greater than 95% of the correlations between channel 1 and scrambled channel 2s.

Costes’ scrambled images were generated by randomly rearranging blocks of the channel-2 image. The size of these blocks was chosen to equal the point spread function (PSF) of the image.

An approximation of Costes’ approach is used by Bitplane’s Imaris and also the Colocalisation Test plugin. For Imaris, a white noise image is smoothed with a Gaussian filter the width of the image’s PSF. The Colocalisation Test plugin generates a randomized image by taking random pixels from the channel-2 image; it then smoothes the image with a Gaussian filter, which is again the width of the image’s PSF.

The Colocalisation Test plugin calculates Pearson’s correlation coefficient for the two selected channels (Robs) and compares this to Pearson’s coefficients for channel 1 against a number of randomized channel-2 images (Rrand).

–Colocalization image processing matlab:

doi:10.1016/S0169-2607(03)00071-3

We developed a simple tool using Matlab to automate the colocalization procedure and to exclude the biased estimations resulting from visual inspections of images. The script in Matlab language code automatically imports confocal images and converts them into arrays. The contrast of all images is uniformly set by linearly reassigning the values of pixel intensities to use the full 8-bit range (0–255). Images are binarized on several threshold levels. The area above a certain threshold level is summed for each channel of the image and for colocalized regions. As a result, count of pixels above several threshold levels in any number of images is saved in an ASCII file. In addition Pearson’s r correlation coefficient is calculated for fluorescence intensities of both confocal channels. Using this approach quick quantitative analysis of colocalization of hundreds of images is possible. In addition, such automated procedure is not biased by the examiner’s subject visualization.

kreft-2004-colocalization

2. Importing Image Files

ImageJ primarily uses TIFF as the image file format. The menu command “File/Save” will save in TIFF format. The menu command “File/Open” will open TIFF files and import a number of other common file formats (e.g. JPEG, GIF, BMP, PGM, PNG). These natively supported files can also be opened by drag-and-dropping the file on to the ImageJ toolbar. MetaMorph *.STK files can also be opened directly.

Several more file formats can be imported via ImageJ plugins (e.g. Biorad, Noran, Zeiss, Leica). When you subsequently save these files within ImageJ they will no longer be in their native format. Bear this in mind; ensure you do not overwrite original data.

There are further file formats such as PNG, PSD (Photoshop), ICO (Windows icon), PICT, which can be imported via the menu command “File/Import/*.PNGJimi Reader… ”.

2.1 Importing Zeiss LSM files

Files acquired on the Zeiss confocal are can be opened directly (with the “Handle Extra File Types” plugin installed) via the “File/Open” menu command, or by dropping them on the ImageJ toolbar. They can also be imported via the “Zeiss LSM Import Panel” which is activated by the menu command “File/Import/*.LSM”. This plugin has the advantage of being able to access extra image information stored with the LSM file, but it is an extra mouse click.

Images are opened as 8-bit colour images with the “no-palette” pseudocolour (!) from the LSM acquisition software. Each channel is imported as a separate image/stack. Lambda stacks are therefore imported as multiple images, not a single stack. They can be converted to a stack with the menu command: “Image/Stacks/Covert Images to stack”.

Once opened, the file information can be accessed and the z/t/lambda information can be irreversibly stamped in to the images or exported to a text file.

2.2 Importing Zeiss ZVI files

ZVI files can be imported via the menu command “File/Import/*.ZVI“. The files are opened as a single stack with the different channels interleaved. The channels can be separated with the “Plugins/Stacks-Shuffling/DeInterleave” plugin.

2.3 Importing Noran SGI file

Noran movies can be opened in several ways:

“File/Import/Noran movie… ” opens the entire movie as an image stack.
“File/Import/Noran Selection… ” allows you to specify a range of frames to be opened as a stack.

The Noran SGI plugins are not bundled with the ImageJ package. To receive them, please contact tonyc@uhnresearch.ca or their author, Greg Joss, so he can keep track of users. Greg Joss gjoss AT bio.mq.edu.au is in the Dept of Biology, Macquarie University, Sydney, Australia.

2.4 Importing Biorad PIC files

Biorad PIC files can be now be imported directly via the menu command “File/Open”. Experimental information, calibration, and other useful information can be accessed via Image/Show Info. Biorad PIC files can also be opened by drag-and-dropping the file on to the ImageJ toolbar. The PIC file is opened with the same LUT with which it was saved in the original acquisition software.

2.5 Importing multiple files from folder

Each time point of an experiment acquired with software such as Perkin Elmer’s UltraVIEW or Scion Image’s time lapse macro is saved by the acquisition software as a single TIF file. The experimental sequence can be imported to ImageJ via the menu command “File/Import/Image Sequence…”.

Locate the directory, click on the first image in the sequence and OK all dialogs. (You may get a couple of error messages while ImageJ tries to open any non-image files in the experimental directory.) The stack will “interleave” the multiple channels you recorded, and can be de-interleaved via “Plugins/Stacks – Shuffling/Deinterleave”.

Selected images that are not the same size can be imported as individual images windows using “File/Import/Selected files to open… ” or as a stack with the “File/Import/Selected files for stack… ”. Unlike the “File/Import/Image Sequence…” function, the images need not be of the same dimensions. If memory is limited, stacks can be opened as Virtual-Stacks with most of the stack remaining on the disk until it is required “File/Import/Disk based stack” .

2.6 Importing Multi-RAW sequence from folder

To form an image, ImageJ needs to know the image dimensions, bit-depth, slice number per file and any extraneous information in the file format (offset and header size). All you really need to tell it is the image dimension in x and y. These values should be obtainable from the software in which the images were acquired. Armed with this information follow these steps:

1. File/Import/Raw…

2. Select experimental directory.

3. Typical values for the dialog box are:

Image type = 16-bit unsigned            (or 8 bit typically)

width and height as determined earlier

offset = 0, number of image = 1, gap = 0, ‘white’ is zero = off

‘Little-endian byte order’ = on, ‘open all files in folder’ = on to open all files in folder.

Non-image files will also be opened and may appear as blank images and need deleting: “Image/Stacks/Delete slice”. The stack will “interleave” the multiple channels you recorded, and can be de-interleaved via “Plugins/Stacks – Shuffling/DeInterleave”.

2.7 Importing AVI and MOV files

There are two plugins which can open uncompressed AVIs and some types of MOV file.

For opening (and writing) QuickTime you need a custom installation of QuickTime to include QT for Java (see section 1.3). QuickTime movies are then opened via “File/Import/*.MOV ”.

Uncompressed AVIs can be opened via “File/Import/*.AVI ”.

2.8 Importing Scanalytics IPLab IPL files

IPLab files can be imported directly by ImageJ. File/Import/*.IPL . Allows Windows IPLab files to be opened directly with the “File/Open” menu command, or drag-and-drop.

The spatial calibration should be imported correctly from your IPLab software.

2.9 Importing Leica SP2 LEI series

Leica SP2 experiments are saved as multiple tiff files in a single folder. This can contain many different series acquired during the one experiment. Along with many tiffs, the folder also contains a text description in the *.TXT files and a Leica proprietary file *.LEI.

Double clicking, drag/dropping of “File/Open“‘ing the *.LEI files should run the Leica TIFF Sequence plugin. Alternatively, run the menu command “File/Import/*.TXT Leica SP2 series” and select the experiment’s TXT file from the open dialog.

A second dialog will open listing the names of the series in the folder. The user can then select those that are to be opened. The appropriate spatial calibration should be read form the txt file and applied to the image. Leica ‘Snapshots’ do not have spatial calibration saved with them. The entry in the TXT file for the series is written to the ‘Notes’ for the image and can be access by the menu command “Image/Show Info…“.

Folders with large numbers of series in could potentially generate a dialog so large that some names are “off screen”. The maximum number of series names per column can be set by running the plugin with the alt-key down.

2.10 Other Import functions

These import plugins import the image data as well as meta-data.

Leica SP- Leica multi-colour images are tiffs. They can be opened as multiple files to a a single stack. Each channel can be imported separately by adding ‘c1′ or c2′ etc. as the import string. Alternatively, they can be all imported to the one stack then separated by de-interleaving them (“Plugins/Stack – shuffling/Deinterleave“).

Olympus Fluoview – available from http://rsb.info.nih.gov/ij/plugins/ucsd.html. Not bundled with the current download.

Animated GI F – This plugin opens an animated GIF file as an RGB stack. Also opens single GIF images.

File/Import/ICS,IDS Image Cytometry Standard file format from Nico Sturman.

File/Import/*.DV *.DV files generated on DeltaVision system format from Fabrice Cordelieres

File/Import/*.ND *.ND files created with MetaMorph’s ‘Multidimensional acquisition”. From Fabrice Cordelieres

• tiff
• volume tiff
• raw
• avi

———————————–

plug-in imageJ

This plugin opens multi-TIFF series acquired with Leica SP2 confocal.

Run the plugin; select the TXT file associated with the TIFF series and select the series to be opened. This information is then passed to the native “Import Sequence” function.

Optional ability to split the channels.

The plugin should apply the spatial calibration found in the TXT file.

ImageJ primarily uses TIFF as the image file format. The menu command “File/Save” will save in TIFF format. The menu command “File/Open” will open TIFF files and import a number of other common file formats (e.g. JPEG, GIF, BMP, PGM, PNG). These natively supported files can also be opened by drag-and-dropping the file on to the ImageJ toolbar. MetaMorph *.STK files can also be opened directly.

Several more file formats can be imported via ImageJ plugins (e.g. Biorad, Noran, Zeiss, Leica). When you subsequently save these files within ImageJ they will no longer be in their native format. Bear this in mind; ensure you do not overwrite original data.

There are further file formats such as PNG, PSD (Photoshop), ICO (Windows icon), PICT, which can be imported via the menu command “File/Import/*.PNGJimi Reader…<!–[if supportFields]> XE “Jimi Reader…:Wayne Rasband and Ulf Dittmer (udittmer at mac.com)” <![endif]–> ”.

exemple pour Zeiss LSM:

Importing Leica SP2 LEI series

Leica SP2 experiments are saved as multiple tiff files in a single folder. This can contain many different series acquired during the one experiment. Along with many tiffs, the folder also contains a text description in the *.TXT files and a Leica proprietary file *.LEI.

Double clicking, drag/dropping of “File/Open“‘ing the *.LEI files should run the Leica TIFF Sequence plugin. Alternatively, run the menu command “File/Import/*.TXT Leica SP2 series” and select the experiment’s TXT file from the open dialog.

A second dialog will open listing the names of the series in the folder. The user can then select those that are to be opened. The appropriate spatial calibration should be read form the txt file and applied to the image. Leica ‘Snapshots’ do not have spatial calibration saved with them. The entry in the TXT file for the series is written to the ‘Notes’ for the image and can be access by the menu command “Image/Show Info…“.

Folders with large numbers of series in could potentially generate a dialog so large that some names are “off screen”. The maximum number of series names per column can be set by running the plugin with the alt-key down.

plug-in download:

http://rsbweb.nih.gov/ij/plugins/leica-tiff.html

2006/02/16:First version
2006/03/02:Fixed error arising from series with similar names containing spaces; errors arising from images with Gray LUT.
2006/03/20:Filenames listed in multiple columns (max number of rows per column can be set by running the plugin with the alt-key down.).

—–author

Author: Tony Collins (tonyc at uhnresearch.ca)
Wright Cell Imaging Facility, Toronto, Canada

http://www.uhnresearch.ca/facilities/wcif/software/Plugins/LeicaTIFF.html

Save to plugins folder; compile and run plugin.
The compiled version is bundled with latest WCIF ImageJ bundle along with modified HandleExtraFileTypes.class (courtesy of Greg Jefferis) to allow double-clicking of the *.LEI file to open the sequence.

Code for HandleExtraFileTypes.java from Greg:

//  Leica SP confocal .lei file handler
        if (name.endsWith(".lei")) {
            int dotIndex = name.lastIndexOf(".");
            if (dotIndex>=0)
                name = name.substring(0, dotIndex);
            path = directory+name+".txt";
            File f = new File(path);
            if(!f.exists()){
                IJ.error("Cannot find the Leica information file: "+path);
                return null;
            }
            IJ.runPlugIn("Leica_TIFF_sequence", path);
            width = IMAGE_OPENED;
            return null;
        }

———————-Huygens Software reads and writes

The Huygens Software reads and writes (among other → File Formats) TIFF series with Leica style numbering if there are more channels (different wavelength), slices or frames (in a Time Series) than in a simple Numbered Tiff series.

An image of four slices and two frames is named with Leica style numbering as follows:

c_t00_z000.tif
c_t00_z001.tif
c_t00_z002.tif
c_t00_z003.tif
c_t01_z000.tif
c_t01_z001.tif
c_t01_z002.tif
c_t01_z003.tif

And an image sTCh of four slices, three frames and two channels:

sTCh_t00_z000_ch00.tif
sTCh_t00_z000_ch01.tif
sTCh_t00_z001_ch00.tif
sTCh_t00_z001_ch01.tif
sTCh_t00_z002_ch00.tif
sTCh_t00_z002_ch01.tif
sTCh_t00_z003_ch00.tif
sTCh_t00_z003_ch01.tif
sTCh_t01_z000_ch00.tif
sTCh_t01_z000_ch01.tif
sTCh_t01_z001_ch00.tif
sTCh_t01_z001_ch01.tif
sTCh_t01_z002_ch00.tif
sTCh_t01_z002_ch01.tif
sTCh_t01_z003_ch00.tif
sTCh_t01_z003_ch01.tif
sTCh_t02_z000_ch00.tif
sTCh_t02_z000_ch01.tif
sTCh_t02_z001_ch00.tif
sTCh_t02_z001_ch01.tif
sTCh_t02_z002_ch00.tif
sTCh_t02_z002_ch01.tif
sTCh_t02_z003_ch00.tif
sTCh_t02_z003_ch01.tif
------------

-----------------------quelques ressources des centres
http://www-ijpb.versailles.inra.fr/fr/lcc/fichiers/equip-sp2.htm
http://www-ijpb.versailles.inra.fr/fr/lcc/fichiers/pdf/instruction-utilisation-SP2.pdf

http://www.itg.uiuc.edu/ms/equipment/microscopes/lscm.htm
leica LCS lite

http://microscopy.unc.edu/How-to/leicasp2/default-viewing.html

http://ijm2.ijm.jussieu.fr/imagerie/fichiers

This page describes how to create volume renderings using my free MRIcro software. You can learn a lot about the brain by viewing axial, coronal and sagittal slices. However, it is often useful to display lesion location on the rendered surface of the brain. Just as each individual has unique finger prints, each brain has a unique sulcal pattern. SPM’s spatial normalization adjusts the size and alignment of the MRI scan, but it does not deliver a precise sulcal match -such an algorithm would create many local distortions (just as an algorithm that attempted to normalize fingerprints from individuals with very different patterns would require many distortions). Volume rendering allows the viewer to grasp the sulcal pattern of the brain, and see lesions in relation to common landmarks. A second advantage for displaying a lesion on a rendered image is that you can specify how ‘deep’ to search beneath the surface to display a lesion, and in this way you can show the cortical damage without the underlying damage to the white matter Note that only showing lesions near the brain’s surface can also be misleading, as it can hide deeper damage. Therefore, it is often best to present surface renderings in conjunction with stereotactically aligned slices.

There are two popular ways to render objects in 3D. Surface rendering treats the object as having a surface of a uniform colour. In surface rendering, shading is used to show the location of a light source – with some regions illuminated while other regions are darker due to shadows. VolumeJ is an excellent example of a surface renderer. The benefit of surface rendering is that it is generally very fast – you only need to manipulate the points on the surface rather than every single voxel.

On the other hand, Volume rendering is a technique used to display a 2D projection of a 3D discretely sampled data set.

A typical 3D data set is a group of 2D slice images acquired by a CT or MRI scanner. Usually these are acquired in a regular pattern (e.g., one slice every millimeter) and usually have a regular number of image pixels in a regular pattern. This is an example of a regular volumetric grid, with each volume element, or voxel represented by a single value that is obtained by sampling the immediate area surrounding the voxel.

To render a 2D projection of the 3D data set, one first needs to define a camera in space relative to the volume. Also, one needs to define the opacity and color of every voxel. This is usually defined using an RGBA (for red, green, blue, alpha) transfer function that defines the RGBA value for every possible voxel value.

A volume may be viewed by extracting surfaces of equal values from the volume and rendering them as polygonal meshes or by rendering the volume directly as a block of data. The Marching Cubes algorithm is a common technique for extracting a surface from volume data.

On the other hand, volume rendering examines the intensity of the objects. So darker tissues (e.g. sulci) appear darker than brighter tissue. Finally, hybrid rendering allows the user to combine these two techniques – first computing a volume rendering and then highlighting regions based on illumination. For example, my software can create volume, surface or combined volume and surface renderings. The images created by MRIcro below illustrates these techniques. The left-most image is a volume rendering, the middle image is a surface rendering, and the image on the right is a hybird.

Surface rendering is by far the most popular approach to rendering objects. One reason for this is that surface rendering can be much quicker than volume rendering (as only the vertexes need to be recomputed following a rotation, while in volume rendering every voxel must be recomputed). However, surface rendering typically has several disadvantages compared to volume rendering: It requires high quality scan and excellent skull extraction to show clean edges. Surface color does not reflect underlying tissue (unless texture maps are used). To get sharp edges, the gray matter is typically eroded, creating an inaccurate image of brain size. The third point is illustrated on the image on the right. Note that with a high air/surface threshold is required to show nice sulcal definition in the surface renderings. However, at these high values the gray matter has been stripped from the image. In contrast, low signal information enhances the definition of sulci for volume renderings.

Most rendering tools add perspective: closer items appear larger than more distant items. Perspective is a strong monocular cue for depth, so this technique creates a powerful illusion of depth. However, in some situations, it is helpful to have perspective free images (‘orthographic rendering’). Orthographic rendering has a couple of advantages: it it can be quicker and it can allow a region to remain a constant size in different views. MRIcro is an orthographic renderer.

One word of caution: Be careful about judging left and right with rendered views. My software retains the left-and right of the image. This is particularly confusing with the coronal view when the head is facing toward you. When we see people facing us, we expect their left to be on our right. With my viewer, their left is on your left.

Installing MRIcro Return to Index

The main MRIcro manual describes how to download and install MRIcro. The software comes with a sample image of the brain.

Creating a rendered image Return to Index

Rendering an image with MRIcro is very straightforward. Load the image you wish to view (use the ‘Open’ command in the file menu). Finally, press the button labelled ’3D’. You will see a new window that allows you to adjust the air/surface threshold (which selects the minimum brightness which will be counted as part of your volume) as well as the surface depth (how many voxels beneath the surface are averaged to determine the surface intensity).

Most rendering tools require very high quality scans (such as the MRI scan above, which came from a single individual who was scanned 27 times). As noted earlier, surface rendering tools in particular have problems with the low resolution or average contrast images that are typically found in the clinic. Fortunately, MRIcro works very well with clinical quality scans. The figures on the right come from single fast scans from a clinical scanner (the whole MPRAGE sequences required 6 minutes with Siemens 1.5T). The image on the left shows a stroke patient.

The images above on the right shows a ‘free rotation’ with a cutaway through the skull. When you select the free rotation view, a new set of controls appear that allow you to select the azimuth and elevation of your viewpoint. A 3D cube illustrates the selected viewpoint – with a crosshair depicting the position of the nose. You can also use the mouse to drag the cube to your desired viewpoint. Finally, the ‘free rotation’ selection allows you to select a ‘cutout’ region to allow you to view inside the surface of the image.

Yoking Images Return to Index

MRIcro is specifically designed to help the user locate and identify the ridges and folds (gyri and sulci) of the human brain. These are often difficult to identify given 2D slices of the brain. By running two copies of my software, you can select a landmark on a rendered view and see the corresponding location on a 3D image (as shown below, note you need to have ‘Yoke’ checked [highlighted in green]).

Overlays Return to Index

MRIcro can also display Overlay images – the figures below illustrate overlays. Typically, these are statistical maps generated by SPM, VoxBo or Activ2000 which show functional regions (computed from PET, SPECT or fMRI scans). I have a web page dedicated to loading overlays with MRIcro.

For volume rendering, you must then make sure the ‘Overlay ROI/Depth’ checkbox is checked. The number next to this check box allows you to specify how deep beneath the surface the software will look for a ROI or Overlay (in voxels). A small value means you will only see surface cortical activations/lesions, while a large value will allow you to see deep activations. For example, in the image on the left below, a skull-stripped brain image was loaded and then a functional map was overlaid.

Left: functional results can be overlayed. Adjusting the depth value allows you to visualise surface or deep activity/lesions.

It is important to mention that MRIcro’s renderings of objects below the brain’s surface are viewpoint dependent. This is illustrated in the figures below. Both regions of interest (lesions) and overlays are mapped based on the viewer’s line of sight. This is very different from mri3dX, which computes the location of objects based on the surface normal (essentially, mri3dX computes a line of sight perpendicular to the plane of the surface). Each of these approaches has its benefits and costs – both are correct, but lead to different results. Note that the location of subcortical objects appears to move when viewpoint changes in MRIcro. On the other hand, deep objects will appear greatly magnified with mri3dX. The rendered image of a brain (above, right) illustrates this difference. Here MRIcro is showing a very deep lesion near the center of the brain. The lesion appears at a different location in each image (SPMers call this a ‘glass brain’ view). A very deep object like this would appear much larger in mri3dX.

Brain Extraction Return to Index

In order to create high quality images of the brain’s surface, you need to strip away the scalp. This is a challenging problem. My software comes with Steve Smith’s automated Brain Extraction Tool [BET], (for citations: Smith, SM (2002) Fast robust automated brain extraction, Human Brain Mapping, 17, 143-155). BET is usually able to accurately extract brain images very effectively. To use BET, you simply click on ‘Skull strip image [for rendering]‘ from the ‘Etc’ menu. You then select the image you want to convert, and give a name for the new stripped image. If you have trouble brain stripping an image, try these techniques:

• You can adjust BET’s fractional intensity threshold. The default is 0.50. Lower numbers lead to smoother brains and a larger estimate of brain size.
• BET starts stripping an image from the center of “gravity” of the volume (think of intensity as mass). If the COG of your head image is not near the centre of the brain, you may have a problem. This is particularly a problem with clinical images that show large portions of the neck. To clip excess slices from an image, choose MRIcro’s ‘Save as [clipped/format]‘ function and set the number of low and high slices you wish to clip (clipping is described in stage 1, Step 1 of my normalization tutorial).

Object Extraction Return to Index

The Brain Extraction Tool described in the previous section is useful for removing a brain from the surrounding scalp. How about removing other objects – for example BET will not work if you wish to extract the image of a torso from surrounding speckle noise. Most scans show a bit of ‘speckle’ in the air surrounding an object (also known as ‘salt and pepper noise’. Most of the time, you can simply adjust the MRIcro’s ‘air/surface’ threshold to eliminate noise. However, sometimes spikes of noise are impossible to eliminate without eroding the surface of the object you want to image. To help, MRIcro (1.36 or later) includes a tool to eliminate air speckles. Load the image and adjust the contrast (often choosing ‘Contrast autobalance’ [Fn5] does a good job, but this often does not work for CT scans). Choose ‘Remove air speckles’ from the ‘Etc’ menu. Adjust the ‘Air/Surface threshold’ so that most noise appers as isolated pixels, while the object you want to extract is virtually all green. Set the Erode and Dilate cycles – usually values of 2-3 for each is about right. Press ‘Go’ and name your new file.

To understand the settings of this command, consider the stages MRIcro uses to despeckle an image. First, consider an image with a few air speckles (figure A below, note a few red speckles in the air). MRIcro first smooths the image by finding the mean of the voxel and the 6 voxels that share a surface with it (B). This usually attenuates any speckles (a median filter would be better, but is much slower). Second, only voxels brighter than the user specified threshold are included in a mask (C). Third, a number of passes of erosion are conducted (D). During each pass, a voxel is eroded if 3 or more of its immediate neighbors are not part of the mask. Fourth, the mask is grown for the number of dilate cycles (E). During each dilation pass, a voxel is grown if any of its immediate neighbors is part of a mask. Note that any cluster of voxels completely eliminated during erosion does not regrow – thus eliminating most noise speckles. Finally, the voxels from the original image are inserted into the masked region (F)

The images to the right show how this technique can be used to effectively extract bone from a CT of an ankle. The right panel shows a standard rendering of the image using a air/surface threshold that shows the bone and hides the surrounding tissue. Note that the ends of the bones are not visible. On the right we see the same image after object extraction (threshold 110, 2 erode cycles, 2 dilate cycles). The extracted rendering appears much clearer than the original.

Maximum Intensity Projections [MIP] Return to Index

So far this web page has described MRIcro’s two primary rendering techniques: volume and surface rendering. However, MRIcro 1.37 and later add a third technique: the maximum intensity projection. This technique simply plots the brightest voxel in the path of a ray traversing the image. The result is a flat looking image that looks a bit like a 2D plain film XRay. This technique is typically a poor choice for most images. The exception is some CAT scans and angiograms. In this case, the MIP is able to identify bright objects embedded inside another object. The images at the right show an MR angiogram of my brain (this image shows an axial view of my circle of willis) and a CT scan of a wrist (note the bright metal pin). To create a MIP, simply select press the ‘MIP’ button in MRIcro’s render window.

Sample Datasets Return to Index

Here are some large sample datasets. The CT scan is perfect for surface renderings, and allows you to change the air/surface threshold to either see the skin or bone as the image surface. This image demonstrates that MRIcro’s rendering can be equally effective with CT scans. Additional images can be on the web. Some nice data sets are a knee, a skull (with EEG leads attached), a bonsai tree, an aneurysm, a foot, a lobster, a high resolution skull, a a fish, and a a teddy bear. All the images I provide for download here have had the voxels outside the object (typically air) set to zero (this improves file compression).

	This CT scan is from the Chapel Hill Volume Rendering Test Dataset. The data is from a General Electric CT scanner at the North Carolina Memorial Hospital. I cropped the image to 208x256x225 voxels. Shift+click here to download (2.7 Mb).
A clinical-quality MRI scan of a healthy individual (1.5T Siemens MPRAGE flip-angle=12-degrees, TR=9.7ms, TE=4.0ms, 1x1x1mm). This image has 180x256x213 voxels. Shift+click here to download (3.4 Mb). Courtesy of Paul Morgan.
	This is a 256x256x110 voxel CT of an engine. This is a popular public domain image. Shift+click here to download (1.5 Mb).
As well as CT and MRI scans, MRIcro can also render high resolution images from laser scanning confocal microscopy. The latest versions of MRIcro automatically read BioRad PIC and Zeiss TIF images. Shift+click to download this image of a daisy pollen granule from Olaf Ronneberger (0.5 Mb).
	Shift+click to download this MRI scan from a mouse embryo taken 13.5 days post conception (0.5 Mb). For more details, visit the home page for this project.
Shift+click to download this MRI arterial angiogram of my brain’s circle of willis (0.5 Mb). This scan was acquired with a 0.2×0.2×0.5mm resolution, then object extracted and rescaled to 0.4mm isotropic (to reduce download time). (3.0T Philips FFE flip-angle=20-degrees, TR=32.7ms, TE=3.4ms, ’3DI INFL HR’). Courtesy of Paul Morgan (see his web page for movies of angiograms and other MRI scans). Amazingly, there is no artificial contrast. Also, note that the veins have been suppressed.

Acknowledgements Return to Index

Tom Womack devised the rapid surface shading algorithm and gave me a lot of tips (he also compiled the version of BET that I distribute). Krish Singh’s brilliant mri3dX inspired me. Its ability to show viewpoint independent functional data makes it a complimentary tool for visualising the brain. Steve Smith’s BET usually turns skull stripping from a tedious effort to an automated process. Earl F. Glynn developed the code for computing a matrix based on azimuth and elevation. This elegant tool allows the user to change their viewpoint without having to worry about gimbal lock problems or confusing controls. MRIcro does not use/require DirectX or OpenGL, for great articles on 3D graphics visit www.delphi3d.net.

Other renderers Return to Index

In addition to MRIcro, a number of freeware programs are available that can display rendered images of the brain (either surface rendering, which treats the surface as a uniform color, or volume rendering, which takes into account the brightness of the material beneath the surface). Both MRIcro and mri3dX inlude Steve Smith’s BET software for extracting the brain from the rest of the image. For the other programs, you will first need to skull-strip your brain images (e.g. with BET or BSE).

Several more file formats can be imported via ImageJ plugins (e.g. Biorad, Noran, Zeiss, Leica). When you subsequently save these files within ImageJ they will no longer be in their native format. Bear this in mind; ensure you do not overwrite original data.

There are further file formats such as PNG, PSD (Photoshop), ICO (Windows icon), PICT, which can be imported via the menu command “File/Import/*.PNGJimi Reader…<!–[if supportFields]> XE “Jimi Reader…:Wayne Rasband and Ulf Dittmer (udittmer at mac.com)” <![endif]–> ”.

La collection de plug-in pour imageJ pour importer:

http://www.uhnresearch.ca/facilities/wcif/imagej/importing_image_files.htm#Import_other

IMPORTING LEICA files:

Leica SP2 experiments are saved as multiple tiff files in a single folder. This can contain many different series acquired during the one experiment. Along with many tiffs, the folder also contains a text description in the *.TXT files and a Leica proprietary file *.LEI.

Double clicking, drag/dropping of “File/Open“‘ing the *.LEI files should run the Leica TIFF Sequence plugin. Alternatively, run the menu command “File/Import/*.TXT Leica SP2 series” and select the experiment’s TXT file from the open dialog.

A second dialog will open listing the names of the series in the folder. The user can then select those that are to be opened. The appropriate spatial calibration should be read form the txt file and applied to the image. Leica ‘Snapshots’ do not have spatial calibration saved with them. The entry in the TXT file for the series is written to the ‘Notes’ for the image and can be access by the menu command “Image/Show Info…“.

Folders with large numbers of series in could potentially generate a dialog so large that some names are “off screen”. The maximum number of series names per column can be set by running the plugin with the alt-key down.

Other Import functions

These import plugins import the image data as well as meta-data.

Leica SP- Leica multi-colour images are tiffs. They can be opened as multiple files to a a single stack. Each channel can be imported separately by adding ‘c1′ or c2′ etc. as the import string. Alternatively, they can be all imported to the one stack then separated by de-interleaving them (“Plugins/Stack – shuffling/Deinterleave“).

Olympus Fluoview – available from http://rsb.info.nih.gov/ij/plugins/ucsd.html. Not bundled with the current download. WCIF collection de plug-in.

Une collection de plug-in pour imageJ:

http://www.uhnresearch.ca/facilities/wcif/imagej/

Installation d’imageJ , aussi issu du monde mac (NIH image; 70000lignes pascal) sur macOSX:

http://rsb.info.nih.gov/ij/docs/install/osx.html